Package: deepgp
Type: Package
Title: Sequential Design for Deep Gaussian Processes using MCMC
Version: 0.2.0
Date: 2020-12-15
Author: Annie Sauer <anniees@vt.edu>
Maintainer: Annie Sauer <anniees@vt.edu>
Depends: R (>= 3.6)
Description: Performs model fitting and sequential design for deep Gaussian
     processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>.  Models 
     extend up to three layers deep; a one layer model is equivalent to typical Gaussian 
     process regression.  Sequential design criteria include integrated mean-squared 
     error (IMSE), active learning Cohn (ALC), and expected 
     improvement (EI).  Covariance structure is based on inverse exponentiated
     squared euclidean distance.  Applicable to noisy and deterministic functions.  
     Incorporates SNOW parallelization and utilizes C under the hood.
License: LGPL
Encoding: UTF-8
NeedsCompilation: yes
Imports: grDevices, graphics, stats, doParallel, foreach, parallel
Suggests: akima, knitr
RoxygenNote: 7.1.1
Packaged: 2020-12-16 16:00:10 UTC; anniesauer
Repository: CRAN
Date/Publication: 2020-12-16 16:50:08 UTC
